Addressing Parameter Uncertainty in Prospective Inventory Modeling

Kurzfassung

Parameter uncertainty, arising from data gaps, inaccuracies, and non-representative data, poses a significant challenge in prospective life cycle assessment (pLCA). This type of uncertainty, widespread in life cycle inventory (LCI) data, remains a prominent challenge for pLCA practitioners. Although this is acknowledged, there is currently no standardized methodology to address parameter uncertainty within a prospective LCI framework. To fill this gap, our study offers a systematic approach to enhance the robustness and transparency of prospective LCI modeling by including uncertainties. A guideline on selecting prospective methods for LCI modeling and approaches to characterize parameter uncertainty is presented. At first, modeling methods commonly used in pLCA are collected and grouped in clusters. Then, a systematic framework to model pLCI data based on findings from literature and our own experience is proposed. Afterwards, the clusters of methods are integrated into the systematic framework to aid the identification and characterization of parameter uncertainty. To demonstrate the applicability of our approach, an emerging energy technology example of a “solid-oxide-electrolyzer-cel (SOEC)l for producing synthetic fuels for aviation in 2040”is provided. Our guideline outlines a structured approach comprising six steps, which can be integrated into the established phases of life cycle assessment (LCA): “goal and scope definition” and “life cycle inventory”. The first step involves defining the assessment’s goal, scope, temporal boundaries, and considering the technology’s maturity level. Step two focuses on collecting data to depict the development of the technology. Steps three through five specifically target parameters likely to undergo significant changes in the future, introducing uncertainties such as variations in manufacturing processes, materials, and component dimensions. Step six addresses external factors influencing the technology, adding uncertainties. The application of our framework to a SOEC example allowed us to identify and characterize the most uncertain parameters affecting the technology’s future development and adoption. Our study provides a structured framework for mapping future technology development, encompassing various stages, critical factors, and inherent uncertainties. This guidance not only aids pLCA practitioners, but also contributes to more transparent and more consistent results.